Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach
This study investigates the factors influencing users' intention to utilise Indonesia's Quick Response Code Indonesian Standard (QRIS). It employed a questionnaire survey with 996 QRIS users in Indonesia as respondents, with response rate 95. 95 %. This study analysed the data using the st...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Telematics and Informatics Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772503024000719 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850041457228906496 |
|---|---|
| author | Rizka Ramayanti Zubir Azhar Nik Hadian Nik Azman |
| author_facet | Rizka Ramayanti Zubir Azhar Nik Hadian Nik Azman |
| author_sort | Rizka Ramayanti |
| collection | DOAJ |
| description | This study investigates the factors influencing users' intention to utilise Indonesia's Quick Response Code Indonesian Standard (QRIS). It employed a questionnaire survey with 996 QRIS users in Indonesia as respondents, with response rate 95. 95 %. This study analysed the data using the structural equation modelling – artificial neural network (SEM-ANN) approach. The data were evaluated using SmartPLS 4.0 and SPSS 26 for artificial neural network (ANN) analysis. The SEM analysis suggests that Habit, Hedonic Motivation, Social Influence, and Price Value are the primary factors influencing QRIS intention and usage in Indonesia. Performance expectancy, effort expectancy, and facilitating factors are not significantly related to users' intention to use QRIS. User intention significantly influences the actual usage of QRIS. The importance-performance matrix analysis (IPMA) test indicates that hedonic desire is the most significant factor in predicting the intention to use QRIS compared to other variables. The findings demonstrated that hedonic motivation was the primary determinant in forecasting the intention to use QRIS. |
| format | Article |
| id | doaj-art-aa4fa3de18544ccdac565febde8da330 |
| institution | DOAJ |
| issn | 2772-5030 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Telematics and Informatics Reports |
| spelling | doaj-art-aa4fa3de18544ccdac565febde8da3302025-08-20T02:55:46ZengElsevierTelematics and Informatics Reports2772-50302025-03-011710018510.1016/j.teler.2024.100185Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approachRizka Ramayanti0Zubir Azhar1Nik Hadian Nik Azman2Accounting Study Program, Faculty of Economics and Business, Universitas Trilogi, Jakarta, Indonesia; Corresponding author.School of Management, Universiti Sains Malaysia, Penang, MalaysiaSchool of Management, Universiti Sains Malaysia, Penang, MalaysiaThis study investigates the factors influencing users' intention to utilise Indonesia's Quick Response Code Indonesian Standard (QRIS). It employed a questionnaire survey with 996 QRIS users in Indonesia as respondents, with response rate 95. 95 %. This study analysed the data using the structural equation modelling – artificial neural network (SEM-ANN) approach. The data were evaluated using SmartPLS 4.0 and SPSS 26 for artificial neural network (ANN) analysis. The SEM analysis suggests that Habit, Hedonic Motivation, Social Influence, and Price Value are the primary factors influencing QRIS intention and usage in Indonesia. Performance expectancy, effort expectancy, and facilitating factors are not significantly related to users' intention to use QRIS. User intention significantly influences the actual usage of QRIS. The importance-performance matrix analysis (IPMA) test indicates that hedonic desire is the most significant factor in predicting the intention to use QRIS compared to other variables. The findings demonstrated that hedonic motivation was the primary determinant in forecasting the intention to use QRIS.http://www.sciencedirect.com/science/article/pii/S2772503024000719QRISArtificial neural networkPLS-SEMIPMA |
| spellingShingle | Rizka Ramayanti Zubir Azhar Nik Hadian Nik Azman Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach Telematics and Informatics Reports QRIS Artificial neural network PLS-SEM IPMA |
| title | Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach |
| title_full | Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach |
| title_fullStr | Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach |
| title_full_unstemmed | Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach |
| title_short | Factors influencing intentions to use QRIS: A two-staged PLS-SEM and ANN approach |
| title_sort | factors influencing intentions to use qris a two staged pls sem and ann approach |
| topic | QRIS Artificial neural network PLS-SEM IPMA |
| url | http://www.sciencedirect.com/science/article/pii/S2772503024000719 |
| work_keys_str_mv | AT rizkaramayanti factorsinfluencingintentionstouseqrisatwostagedplssemandannapproach AT zubirazhar factorsinfluencingintentionstouseqrisatwostagedplssemandannapproach AT nikhadiannikazman factorsinfluencingintentionstouseqrisatwostagedplssemandannapproach |